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cs.AI2025
Agent2World: Learning to Generate Symbolic World Models via Adaptive Multi-Agent Feedback
Mengkang Hu, Bowei Xia, Yuran Wu +9
Symbolic world models (e.g., PDDL domains or executable simulators) are central to model-based planning, but training LLMs to generate such world models is limited by the lack of l…
cs.CV2025
Synthetic Curriculum Reinforces Compositional Text-to-Image Generation
Shijian Wang, Runhao Fu, Siyi Zhao +6
Text-to-Image (T2I) generation has long been an open problem, with compositional synthesis remaining particularly challenging. This task requires accurate rendering of complex scen…
cs.CV2025
Video-Thinker: Sparking "Thinking with Videos" via Reinforcement Learning
Shijian Wang, Jiarui Jin, Xingjian Wang +6
Recent advances in image reasoning methods, particularly "Thinking with Images", have demonstrated remarkable success in Multimodal Large Language Models (MLLMs); however, this dyn…